{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional Networks\n",
    "So far we have worked with deep fully-connected networks, using them to explore different optimization strategies and network architectures. Fully-connected networks are a good testbed for experimentation because they are very computationally efficient, but in practice all state-of-the-art results use convolutional networks instead.\n",
    "\n",
    "First you will implement several layer types that are used in convolutional networks. You will then use these layers to train a convolutional network on the CIFAR-10 dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.cnn import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient_array, eval_numerical_gradient\n",
    "from cs231n.layers import *\n",
    "from cs231n.fast_layers import *\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" returns relative error \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_val:  (1000, 3, 32, 32)\n",
      "X_train:  (49000, 3, 32, 32)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "y_train:  (49000,)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive forward pass\n",
    "The core of a convolutional network is the convolution operation. In the file `cs231n/layers.py`, implement the forward pass for the convolution layer in the function `conv_forward_naive`. \n",
    "\n",
    "You don't have to worry too much about efficiency at this point; just write the code in whatever way you find most clear.\n",
    "\n",
    "You can test your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_naive\n",
      "difference:  2.21214764175e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "w_shape = (3, 3, 4, 4)\n",
    "x = np.linspace(-0.1, 0.5, num=np.prod(x_shape)).reshape(x_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=np.prod(w_shape)).reshape(w_shape)\n",
    "b = np.linspace(-0.1, 0.2, num=3)\n",
    "\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "out, _ = conv_forward_naive(x, w, b, conv_param)\n",
    "correct_out = np.array([[[[-0.08759809, -0.10987781],\n",
    "                           [-0.18387192, -0.2109216 ]],\n",
    "                          [[ 0.21027089,  0.21661097],\n",
    "                           [ 0.22847626,  0.23004637]],\n",
    "                          [[ 0.50813986,  0.54309974],\n",
    "                           [ 0.64082444,  0.67101435]]],\n",
    "                         [[[-0.98053589, -1.03143541],\n",
    "                           [-1.19128892, -1.24695841]],\n",
    "                          [[ 0.69108355,  0.66880383],\n",
    "                           [ 0.59480972,  0.56776003]],\n",
    "                          [[ 2.36270298,  2.36904306],\n",
    "                           [ 2.38090835,  2.38247847]]]])\n",
    "\n",
    "# Compare your output to ours; difference should be around 2e-8\n",
    "print('Testing conv_forward_naive')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Aside: Image processing via convolutions\n",
    "\n",
    "As fun way to both check your implementation and gain a better understanding of the type of operation that convolutional layers can perform, we will set up an input containing two images and manually set up filters that perform common image processing operations (grayscale conversion and edge detection). The convolution forward pass will apply these operations to each of the input images. We can then visualize the results as a sanity check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
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UHN4ryAolQgwgc1BHAnI22ScAMmhjNeBchzhF1eE8aHQ2Y6dHbNIrwKDLrMqQvgCRAL0m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M1Erz0fZRLyuYBiMU8h5+NrnRJ0yuAFPoE3Xzwx5Ap5myahUCikgQAwB3nz9B0OmxBOppgk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\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UxCYej3P9+nWazWa303bHYX1kZIRarcbY2Bh+vx+73a4216tXr/KVr3yFhYUFAoEAf/mX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\nHBcvXlSCe6FNhfIQSxiZQSkUimiFhE7V6XTKeV3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8oNvt8uu//uv0+32CwSA3b95kYmKC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\nU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7PZZDKZ6HCeSGGI0ySEfgk9CiexUqlopMq81uZ5y/fSt3X2+VTx3qyeIVplkjEqQrKS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\nf5b/jewK/EFcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd630b0f090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.misc import imread, imresize\n",
    "\n",
    "kitten, puppy = imread('kitten.jpg'), imread('puppy.jpg')\n",
    "# kitten is wide, and puppy is already square\n",
    "d = kitten.shape[1] - kitten.shape[0]\n",
    "kitten_cropped = kitten[:, d//2:-d//2, :]\n",
    "\n",
    "img_size = 200   # Make this smaller if it runs too slow\n",
    "x = np.zeros((2, 3, img_size, img_size))\n",
    "x[0, :, :, :] = imresize(puppy, (img_size, img_size)).transpose((2, 0, 1))\n",
    "x[1, :, :, :] = imresize(kitten_cropped, (img_size, img_size)).transpose((2, 0, 1))\n",
    "\n",
    "# Set up a convolutional weights holding 2 filters, each 3x3\n",
    "w = np.zeros((2, 3, 3, 3))\n",
    "\n",
    "# The first filter converts the image to grayscale.\n",
    "# Set up the red, green, and blue channels of the filter.\n",
    "w[0, 0, :, :] = [[0, 0, 0], [0, 0.3, 0], [0, 0, 0]]\n",
    "w[0, 1, :, :] = [[0, 0, 0], [0, 0.6, 0], [0, 0, 0]]\n",
    "w[0, 2, :, :] = [[0, 0, 0], [0, 0.1, 0], [0, 0, 0]]\n",
    "\n",
    "# Second filter detects horizontal edges in the blue channel.\n",
    "w[1, 2, :, :] = [[1, 2, 1], [0, 0, 0], [-1, -2, -1]]\n",
    "\n",
    "# Vector of biases. We don't need any bias for the grayscale\n",
    "# filter, but for the edge detection filter we want to add 128\n",
    "# to each output so that nothing is negative.\n",
    "b = np.array([0, 128])\n",
    "\n",
    "# Compute the result of convolving each input in x with each filter in w,\n",
    "# offsetting by b, and storing the results in out.\n",
    "out, _ = conv_forward_naive(x, w, b, {'stride': 1, 'pad': 1})\n",
    "\n",
    "def imshow_noax(img, normalize=True):\n",
    "    \"\"\" Tiny helper to show images as uint8 and remove axis labels \"\"\"\n",
    "    if normalize:\n",
    "        img_max, img_min = np.max(img), np.min(img)\n",
    "        img = 255.0 * (img - img_min) / (img_max - img_min)\n",
    "    plt.imshow(img.astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "\n",
    "# Show the original images and the results of the conv operation\n",
    "plt.subplot(2, 3, 1)\n",
    "imshow_noax(puppy, normalize=False)\n",
    "plt.title('Original image')\n",
    "plt.subplot(2, 3, 2)\n",
    "imshow_noax(out[0, 0])\n",
    "plt.title('Grayscale')\n",
    "plt.subplot(2, 3, 3)\n",
    "imshow_noax(out[0, 1])\n",
    "plt.title('Edges')\n",
    "plt.subplot(2, 3, 4)\n",
    "imshow_noax(kitten_cropped, normalize=False)\n",
    "plt.subplot(2, 3, 5)\n",
    "imshow_noax(out[1, 0])\n",
    "plt.subplot(2, 3, 6)\n",
    "imshow_noax(out[1, 1])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolution: Naive backward pass\n",
    "Implement the backward pass for the convolution operation in the function `conv_backward_naive` in the file `cs231n/layers.py`. Again, you don't need to worry too much about computational efficiency.\n",
    "\n",
    "When you are done, run the following to check your backward pass with a numeric gradient check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_backward_naive function\n",
      "dx error:  1.15980316116e-08\n",
      "dw error:  2.24712647485e-10\n",
      "db error:  3.3726400665e-11\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(4, 3, 5, 5)\n",
    "w = np.random.randn(2, 3, 3, 3)\n",
    "b = np.random.randn(2,)\n",
    "dout = np.random.randn(4, 2, 5, 5)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_forward_naive(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_forward_naive(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_forward_naive(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "out, cache = conv_forward_naive(x, w, b, conv_param)\n",
    "dx, dw, db = conv_backward_naive(dout, cache)\n",
    "\n",
    "# Your errors should be around 1e-8'\n",
    "print('Testing conv_backward_naive function')\n",
    "print('dx error: ', rel_error(dx, dx_num))\n",
    "print('dw error: ', rel_error(dw, dw_num))\n",
    "print('db error: ', rel_error(db, db_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive forward\n",
    "Implement the forward pass for the max-pooling operation in the function `max_pool_forward_naive` in the file `cs231n/layers.py`. Again, don't worry too much about computational efficiency.\n",
    "\n",
    "Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_forward_naive function:\n",
      "difference:  4.16666651573e-08\n"
     ]
    }
   ],
   "source": [
    "x_shape = (2, 3, 4, 4)\n",
    "x = np.linspace(-0.3, 0.4, num=np.prod(x_shape)).reshape(x_shape)\n",
    "pool_param = {'pool_width': 2, 'pool_height': 2, 'stride': 2}\n",
    "\n",
    "out, _ = max_pool_forward_naive(x, pool_param)\n",
    "\n",
    "correct_out = np.array([[[[-0.26315789, -0.24842105],\n",
    "                          [-0.20421053, -0.18947368]],\n",
    "                         [[-0.14526316, -0.13052632],\n",
    "                          [-0.08631579, -0.07157895]],\n",
    "                         [[-0.02736842, -0.01263158],\n",
    "                          [ 0.03157895,  0.04631579]]],\n",
    "                        [[[ 0.09052632,  0.10526316],\n",
    "                          [ 0.14947368,  0.16421053]],\n",
    "                         [[ 0.20842105,  0.22315789],\n",
    "                          [ 0.26736842,  0.28210526]],\n",
    "                         [[ 0.32631579,  0.34105263],\n",
    "                          [ 0.38526316,  0.4       ]]]])\n",
    "\n",
    "# Compare your output with ours. Difference should be around 1e-8.\n",
    "print('Testing max_pool_forward_naive function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Max pooling: Naive backward\n",
    "Implement the backward pass for the max-pooling operation in the function `max_pool_backward_naive` in the file `cs231n/layers.py`. You don't need to worry about computational efficiency.\n",
    "\n",
    "Check your implementation with numeric gradient checking by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing max_pool_backward_naive function:\n",
      "dx error:  3.27562514223e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(3, 2, 8, 8)\n",
    "dout = np.random.randn(3, 2, 4, 4)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: max_pool_forward_naive(x, pool_param)[0], x, dout)\n",
    "\n",
    "out, cache = max_pool_forward_naive(x, pool_param)\n",
    "dx = max_pool_backward_naive(dout, cache)\n",
    "\n",
    "# Your error should be around 1e-12\n",
    "print('Testing max_pool_backward_naive function:')\n",
    "print('dx error: ', rel_error(dx, dx_num))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Fast layers\n",
    "Making convolution and pooling layers fast can be challenging. To spare you the pain, we've provided fast implementations of the forward and backward passes for convolution and pooling layers in the file `cs231n/fast_layers.py`.\n",
    "\n",
    "The fast convolution implementation depends on a Cython extension; to compile it you need to run the following from the `cs231n` directory:\n",
    "\n",
    "```bash\n",
    "python setup.py build_ext --inplace\n",
    "```\n",
    "\n",
    "The API for the fast versions of the convolution and pooling layers is exactly the same as the naive versions that you implemented above: the forward pass receives data, weights, and parameters and produces outputs and a cache object; the backward pass recieves upstream derivatives and the cache object and produces gradients with respect to the data and weights.\n",
    "\n",
    "**NOTE:** The fast implementation for pooling will only perform optimally if the pooling regions are non-overlapping and tile the input. If these conditions are not met then the fast pooling implementation will not be much faster than the naive implementation.\n",
    "\n",
    "You can compare the performance of the naive and fast versions of these layers by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_forward_fast:\n",
      "Naive: 3.669734s\n",
      "Fast: 0.006410s\n",
      "Speedup: 572.490516x\n",
      "Difference:  4.92640785149e-11\n",
      "\n",
      "Testing conv_backward_fast:\n",
      "Naive: 4.580569s\n",
      "Fast: 0.006602s\n",
      "Speedup: 693.810227x\n",
      "dx difference:  1.94976477535e-11\n",
      "dw difference:  3.681156828e-13\n",
      "db difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import conv_forward_fast, conv_backward_fast\n",
    "from time import time\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 31, 31)\n",
    "w = np.random.randn(25, 3, 3, 3)\n",
    "b = np.random.randn(25,)\n",
    "dout = np.random.randn(100, 25, 16, 16)\n",
    "conv_param = {'stride': 2, 'pad': 1}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = conv_forward_naive(x, w, b, conv_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = conv_forward_fast(x, w, b, conv_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing conv_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('Difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive, dw_naive, db_naive = conv_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast, dw_fast, db_fast = conv_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting conv_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('Fast: %fs' % (t2 - t1))\n",
    "print('Speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))\n",
    "print('dw difference: ', rel_error(dw_naive, dw_fast))\n",
    "print('db difference: ', rel_error(db_naive, db_fast))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing pool_forward_fast:\n",
      "Naive: 0.005478s\n",
      "fast: 0.001702s\n",
      "speedup: 3.218378x\n",
      "difference:  0.0\n",
      "\n",
      "Testing pool_backward_fast:\n",
      "Naive: 0.844384s\n",
      "speedup: 96.281073x\n",
      "dx difference:  0.0\n"
     ]
    }
   ],
   "source": [
    "from cs231n.fast_layers import max_pool_forward_fast, max_pool_backward_fast\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(100, 3, 32, 32)\n",
    "dout = np.random.randn(100, 3, 16, 16)\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "t0 = time()\n",
    "out_naive, cache_naive = max_pool_forward_naive(x, pool_param)\n",
    "t1 = time()\n",
    "out_fast, cache_fast = max_pool_forward_fast(x, pool_param)\n",
    "t2 = time()\n",
    "\n",
    "print('Testing pool_forward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('fast: %fs' % (t2 - t1))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('difference: ', rel_error(out_naive, out_fast))\n",
    "\n",
    "t0 = time()\n",
    "dx_naive = max_pool_backward_naive(dout, cache_naive)\n",
    "t1 = time()\n",
    "dx_fast = max_pool_backward_fast(dout, cache_fast)\n",
    "t2 = time()\n",
    "\n",
    "print('\\nTesting pool_backward_fast:')\n",
    "print('Naive: %fs' % (t1 - t0))\n",
    "print('speedup: %fx' % ((t1 - t0) / (t2 - t1)))\n",
    "print('dx difference: ', rel_error(dx_naive, dx_fast))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Convolutional \"sandwich\" layers\n",
    "Previously we introduced the concept of \"sandwich\" layers that combine multiple operations into commonly used patterns. In the file `cs231n/layer_utils.py` you will find sandwich layers that implement a few commonly used patterns for convolutional networks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu_pool\n",
      "dx error:  9.59113262192e-09\n",
      "dw error:  5.80239113733e-09\n",
      "db error:  1.01463434118e-09\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_pool_forward, conv_relu_pool_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 16, 16)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n",
    "\n",
    "out, cache = conv_relu_pool_forward(x, w, b, conv_param, pool_param)\n",
    "dx, dw, db = conv_relu_pool_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_pool_forward(x, w, b, conv_param, pool_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu_pool')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing conv_relu:\n",
      "dx error:  1.52186199803e-09\n",
      "dw error:  2.7020226461e-10\n",
      "db error:  1.45127239359e-10\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import conv_relu_forward, conv_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 8, 8)\n",
    "w = np.random.randn(3, 3, 3, 3)\n",
    "b = np.random.randn(3,)\n",
    "dout = np.random.randn(2, 3, 8, 8)\n",
    "conv_param = {'stride': 1, 'pad': 1}\n",
    "\n",
    "out, cache = conv_relu_forward(x, w, b, conv_param)\n",
    "dx, dw, db = conv_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: conv_relu_forward(x, w, b, conv_param)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: conv_relu_forward(x, w, b, conv_param)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: conv_relu_forward(x, w, b, conv_param)[0], b, dout)\n",
    "\n",
    "print('Testing conv_relu:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Three-layer ConvNet\n",
    "Now that you have implemented all the necessary layers, we can put them together into a simple convolutional network.\n",
    "\n",
    "Open the file `cs231n/classifiers/cnn.py` and complete the implementation of the `ThreeLayerConvNet` class. Run the following cells to help you debug:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Sanity check loss\n",
    "After you build a new network, one of the first things you should do is sanity check the loss. When we use the softmax loss, we expect the loss for random weights (and no regularization) to be about `log(C)` for `C` classes. When we add regularization this should go up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initial loss (no regularization):  2.30258318667\n",
      "Initial loss (with regularization):  2.50834247355\n"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet()\n",
    "\n",
    "N = 50\n",
    "X = np.random.randn(N, 3, 32, 32)\n",
    "y = np.random.randint(10, size=N)\n",
    "\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (no regularization): ', loss)\n",
    "\n",
    "model.reg = 0.5\n",
    "loss, grads = model.loss(X, y)\n",
    "print('Initial loss (with regularization): ', loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Gradient check\n",
    "After the loss looks reasonable, use numeric gradient checking to make sure that your backward pass is correct. When you use numeric gradient checking you should use a small amount of artifical data and a small number of neurons at each layer. Note: correct implementations may still have relative errors up to 1e-2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W1 max relative error: 1.380104e-04\n",
      "W2 max relative error: 1.822723e-02\n",
      "W3 max relative error: 3.064049e-04\n",
      "b1 max relative error: 3.477652e-05\n",
      "b2 max relative error: 2.516375e-03\n",
      "b3 max relative error: 7.945660e-10\n"
     ]
    }
   ],
   "source": [
    "num_inputs = 2\n",
    "input_dim = (3, 16, 16)\n",
    "reg = 0.0\n",
    "num_classes = 10\n",
    "np.random.seed(231)\n",
    "X = np.random.randn(num_inputs, *input_dim)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "model = ThreeLayerConvNet(num_filters=3, filter_size=3,\n",
    "                          input_dim=input_dim, hidden_dim=7,\n",
    "                          dtype=np.float64)\n",
    "loss, grads = model.loss(X, y)\n",
    "for param_name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    param_grad_num = eval_numerical_gradient(f, model.params[param_name], verbose=False, h=1e-6)\n",
    "    e = rel_error(param_grad_num, grads[param_name])\n",
    "    print('%s max relative error: %e' % (param_name, rel_error(param_grad_num, grads[param_name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Overfit small data\n",
    "A nice trick is to train your model with just a few training samples. You should be able to overfit small datasets, which will result in very high training accuracy and comparatively low validation accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 30) loss: 2.414060\n",
      "(Epoch 0 / 15) train acc: 0.190000; val_acc: 0.128000\n",
      "(Iteration 2 / 30) loss: 2.609504\n",
      "(Epoch 1 / 15) train acc: 0.230000; val_acc: 0.094000\n",
      "(Iteration 3 / 30) loss: 2.113380\n",
      "(Iteration 4 / 30) loss: 1.971811\n",
      "(Epoch 2 / 15) train acc: 0.310000; val_acc: 0.098000\n",
      "(Iteration 5 / 30) loss: 1.676728\n",
      "(Iteration 6 / 30) loss: 1.801782\n",
      "(Epoch 3 / 15) train acc: 0.570000; val_acc: 0.191000\n",
      "(Iteration 7 / 30) loss: 1.652683\n",
      "(Iteration 8 / 30) loss: 1.598651\n",
      "(Epoch 4 / 15) train acc: 0.570000; val_acc: 0.194000\n",
      "(Iteration 9 / 30) loss: 1.070849\n",
      "(Iteration 10 / 30) loss: 1.408982\n",
      "(Epoch 5 / 15) train acc: 0.740000; val_acc: 0.188000\n",
      "(Iteration 11 / 30) loss: 0.816042\n",
      "(Iteration 12 / 30) loss: 0.807953\n",
      "(Epoch 6 / 15) train acc: 0.820000; val_acc: 0.256000\n",
      "(Iteration 13 / 30) loss: 0.971160\n",
      "(Iteration 14 / 30) loss: 0.568949\n",
      "(Epoch 7 / 15) train acc: 0.860000; val_acc: 0.236000\n",
      "(Iteration 15 / 30) loss: 0.394380\n",
      "(Iteration 16 / 30) loss: 0.401405\n",
      "(Epoch 8 / 15) train acc: 0.910000; val_acc: 0.194000\n",
      "(Iteration 17 / 30) loss: 0.723469\n",
      "(Iteration 18 / 30) loss: 0.258560\n",
      "(Epoch 9 / 15) train acc: 0.910000; val_acc: 0.169000\n",
      "(Iteration 19 / 30) loss: 0.242372\n",
      "(Iteration 20 / 30) loss: 0.235556\n",
      "(Epoch 10 / 15) train acc: 0.940000; val_acc: 0.199000\n",
      "(Iteration 21 / 30) loss: 0.212628\n",
      "(Iteration 22 / 30) loss: 0.126721\n",
      "(Epoch 11 / 15) train acc: 0.940000; val_acc: 0.213000\n",
      "(Iteration 23 / 30) loss: 0.113127\n",
      "(Iteration 24 / 30) loss: 0.270010\n",
      "(Epoch 12 / 15) train acc: 0.970000; val_acc: 0.204000\n",
      "(Iteration 25 / 30) loss: 0.067624\n",
      "(Iteration 26 / 30) loss: 0.081088\n",
      "(Epoch 13 / 15) train acc: 1.000000; val_acc: 0.207000\n",
      "(Iteration 27 / 30) loss: 0.029606\n",
      "(Iteration 28 / 30) loss: 0.051089\n",
      "(Epoch 14 / 15) train acc: 1.000000; val_acc: 0.209000\n",
      "(Iteration 29 / 30) loss: 0.027549\n",
      "(Iteration 30 / 30) loss: 0.025578\n",
      "(Epoch 15 / 15) train acc: 1.000000; val_acc: 0.209000\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "\n",
    "num_train = 100\n",
    "small_data = {\n",
    "  'X_train': data['X_train'][:num_train],\n",
    "  'y_train': data['y_train'][:num_train],\n",
    "  'X_val': data['X_val'],\n",
    "  'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "model = ThreeLayerConvNet(weight_scale=1e-2)\n",
    "\n",
    "solver = Solver(model, small_data,\n",
    "                num_epochs=15, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=1)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "Plotting the loss, training accuracy, and validation accuracy should show clear overfitting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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AIAOQlZIVpNjGCUAqEMgAZKVkBSm2cQKQCgQyAFkpWUGKbZwApAKBDEBWSlaQmj21SHdc\nPVlFhfkySUWF+QzoB5B0YW6dBACRSeZ+mGzjBCBsBDIAWYsgBSBT0GUJAAAQMVrIACCDsGtA+uPf\nCF1BIAOADNFy14DGxW4l8YafJvg3QlcRyAAgQ7S32G1n3uyT1YJDS9DxkvVvhO6HQAYAGSIZi90m\nqwWHlqDWsbMDuopB/QCQIZKx2G2ytpRK1v0sWFquGXc+rXG3/kUz7ny603uNpht2dkBXEcgAIEMk\nY7HbZLXgJLO17kQ3gE8n7OyAriKQAUCGSMauAclqwUmn1rp0ws4O6CrGkAFABjnRxW7nzixpNvZL\n6loLTjLuJ1vHW7EgMbqCQAYA3UiytpRKxv2MKsxXeSvhi/FW6I7M3aOuoVNKS0u9rKws6jIAACeo\n5UxNKWhlo4sP2cTMlrh7aUfX0UIGAIhEMjeAZ000ZDoCGQAgMskYb5XMtdUIdYgKsywBABktGbM1\ns3EJDmQWWsgAABktGbM1s3XLI1r9MgeBDACQ0ZIxWzPdluBIRpBie6vMQpclACCjJWN1/HTa8ihZ\n3afptvButm2TlWy0kAEAMloyZmsma8HcZEhW92myWv3SqbUum7tgCWQAgIx3orM1k7kEx4lKVpBK\nRldusoJUMkJmMrtg0zHYhdplaWaXmtlaM1tvZre2cntvM/t97PaXzaw4zHoAAGjL7KlFev7Wi/TW\nne/T87deFNkbdLK6T5PRlZusbs+wJ150RrrOqA0tkJlZjqSfSbpM0kRJ15nZxBaX3SBpr7ufKulH\nkn4QVj0AAGSCZAQpKTkbnSezta4z58OsJd3G1jUKs8tyuqT17r5RkszsYUmzJK2Ku2aWpNtiXz8q\n6admZp5p+zkBAJAkyew+PdGu3GTtN5qMMXrJqiXdZtQ2CjOQFUnaEne8VdK5bV3j7nVmtk/SYEm7\n4i8yszmS5kjS2LFjw6oXAIC0kIwdDJIhWZMd0mniRbpuap8Rg/rd/V5J90rB5uIRlwMAQLeQTq11\nyaolnWbUxgszkJVLGhN3PDp2rrVrtppZT0kDJO0OsSYAANAJ6dJaJyWnlnSaURsvzED2qqTxZjZO\nQfD6sKSPtLjmcUmfkPSipA9KeprxYwAAIEzpFDIbhRbIYmPCbpK0UFKOpF+7+0ozu11Smbs/LulX\nku43s/WS9igIbQAAAN1KqGPI3P0JSU+0OPetuK8PS/pQmDUAAACkO/ayBAAAiBiBDAAAIGKWaWPo\nzaxS0uYUPNQQtVgPDUnDcxsenttw8fyGh+c2XDy/4enouT3J3Yd2dCcZF8hSxczK3L006jqyEc9t\neHhuw8XzGx6e23Dx/IYnWc8tXZYAAAARI5ABAABEjEDWtnujLiCL8dyGh+c2XDy/4eG5DRfPb3iS\n8twyhgwAACBitJABAABEjEDWgpldamZrzWy9md0adT3Zxsw2mdlyM3vdzMqirieTmdmvzazCzFbE\nnRtkZk+Z2ZuxzwOjrDGTtfH83mZm5bHX7+tmdnmUNWYqMxtjZovMbJWZrTSzW2Lnef2eoHaeW167\nSWBmeWb2ipm9EXt+vxM7P87MXo5lh9+bWa9O3zddlseYWY6kdZLeK2mrgg3Sr3P3VZEWlkXMbJOk\nUndnPZwTZGb/IumApPvc/YzYuX+XtMfd74z9QTHQ3b8WZZ2Zqo3n9zZJB9z9/4+ytkxnZiMljXT3\n18ysQNISSbMlXS9evyeknef2GvHaPWFmZpL6uvsBM8uV9JykWyT9H0nz3f1hM7tH0hvufndn7psW\nsuamS1rv7hvd/aikhyXNirgmoFXu/qykPS1Oz5L0u9jXv1Pwixhd0MbziyRw9+3u/lrs62pJqyUV\nidfvCWvnuUUSeOBA7DA39uGSLpL0aOx8l167BLLmiiRtiTveKl7IyeaS/mZmS8xsTtTFZKHh7r49\n9vUOScOjLCZL3WRmy2JdmnSpnSAzK5Y0VdLL4vWbVC2eW4nXblKYWY6ZvS6pQtJTkjZIqnL3utgl\nXcoOBDKk2gXufrakyyR9IdYthBB4MB6BMQnJdbekUyRNkbRd0n9EW05mM7N+kh6T9CV33x9/G6/f\nE9PKc8trN0ncvd7dp0garaBnbUIy7pdA1ly5pDFxx6Nj55Ak7l4e+1wh6Q8KXsxInp2xMSSNY0kq\nIq4nq7j7ztgv4wZJ/y1ev10WG3/zmKQH3H1+7DSv3yRo7bnltZt87l4laZGk8yUVmlnP2E1dyg4E\nsuZelTQ+Nluil6QPS3o84pqyhpn1jQ0ylZn1lXSJpBXtfxc66XFJn4h9/QlJf4ywlqzTGBZi3i9e\nv10SGxj9K0mr3f2HcTfx+j1BbT23vHaTw8yGmllh7Ot8BZMAVysIZh+MXdal1y6zLFuITQX+T0k5\nkn7t7t+PuKSsYWYnK2gVk6Sekh7k+e06M3tI0oWShkjaKenbkhZIekTSWEmbJV3j7gxM74I2nt8L\nFXT5uKRNkj4bN+YJCTKzCyT9U9JySQ2x019XMNaJ1+8JaOe5vU68dk+YmZ2pYNB+joJGrUfc/fbY\n+9vDkgZJWirpY+5+pFP3TSADAACIFl2WAAAAESOQAQAARIxABgAAEDECGQAAQMQIZAAAABEjkAHI\nSGb2QuxzsZl9JMn3/fXWHgsAwsKyFwAympldKOnf3P2KTnxPz7h951q7/YC790tGfQCQCFrIAGQk\nMzsQ+/JOSe80s9fN7MuxjX/vMrNXYxspfzZ2/YVm9k8ze1zSqti5BbGN7lc2bnZvZndKyo/d3wPx\nj2WBu8xshZktN7Nr4+57sZk9amZrzOyB2IrpAJCQnh1fAgBp7VbFtZDFgtU+d59mZr0lPW9mf4td\ne7akM9z9rdjxp9x9T2wLlFfN7DF3v9XMboptHtzS1QpWOz9LwQr+r5rZs7HbpkqaJGmbpOclzZD0\nXPJ/XADZiBYyANnmEkn/amavK9iKZ7Ck8bHbXokLY5J0s5m9IeklSWPirmvLBZIeim3SvFPSM5Km\nxd331tjmza9LKk7KTwOgW6CFDEC2MUlfdPeFzU4GY80Otjh+j6Tz3f2QmS2WlHcCjxu/b129+P0K\noBNoIQOQ6aolFcQdL5T0OTPLlSQzO83M+rbyfQMk7Y2FsQmSzou7rbbx+1v4p6RrY+PUhkr6F0mv\nJOWnANCt8RccgEy3TFJ9rOvxt5J+rKC78LXYwPpKSbNb+b4nJd1oZqslrVXQbdnoXknLzOw1d/9o\n3Pk/SDpf0huSXNJX3X1HLNABQJex7AUAAEDE6LIEAACIGIEMAAAgYgQyAACAiBHIAAAAIkYgAwAA\niBiBDAAAIGIEMgAAgIgRyAAAACJGIAMAAIhYxm2dNGTIEC8uLo66DAAAgA4tWbJkl7sP7ei6jAtk\nxcXFKisri7oMAACADpnZ5kSuo8sSAAAgYgQyAACAiBHIAAAAIpZxY8haU1tbq61bt+rw4cNRlxK6\nvLw8jR49Wrm5uVGXAgAAkiS0QGZmv5Z0haQKdz+jldtN0o8lXS7pkKTr3f21rjzW1q1bVVBQoOLi\nYgV3m53cXbt379bWrVs1bty4qMsBAGSIBUvLddfCtdpWVaNRhfmaO7NEs6cWUUfEdcQLs8vyt5Iu\nbef2yySNj33MkXR3Vx/o8OHDGjx4cFaHMUkyMw0ePLhbtAQCAJJjwdJyzZu/XOVVNXJJ5VU1mjd/\nuRYsLaeOCOtoKbQWMnd/1syK27lklqT73N0lvWRmhWY20t23d+Xxsj2MNeouPycA4MQdrWvQ/31i\ntWpq65udr6mt17z5y/XMusqU1fLkih1pXcddC9dG2koW5RiyIklb4o63xs4dF8jMbI6CVjSNHTs2\nJcUBAJAp3F3lVTVau6Naa3ZUa23sY0PlAdU1eKvfU1NbryWb96asxpYhKN3q2FZVk7IaWpMRg/rd\n/V5J90pSaWlp66+sCFVVVenBBx/U5z//+U593+WXX64HH3xQhYWFIVUGAMg2+w7Vas2O/Vq781j4\nWrejWtVH6pquKSrMV8mIAl10+jA9/Mrb2nuo9rj7KSrM17NffXfK6p5x59MqbyX0pEsdowrzU1ZD\na6IMZOWSxsQdj46dC12yB/NVVVXp5z//+XGBrK6uTj17tv0UP/HEE11+TABAdjtSV6/1FQeaWrsa\nw9eO/cfGEffP66kJI/pr9tQilYwo0IQRBTptRIH65x2biV8yvEDz5i9v1jKUn5ujuTNLUvrzzJ1Z\nQh3tiDKQPS7pJjN7WNK5kvZ1dfxYZzQO5mv8h2gczCepy6Hs1ltv1YYNGzRlyhTl5uYqLy9PAwcO\n1Jo1a7Ru3TrNnj1bW7Zs0eHDh3XLLbdozpw5ko5tA3XgwAFddtlluuCCC/TCCy+oqKhIf/zjH5Wf\nH21aBwCcmEQaABoaXFv31gStXjuqtWZnELze2nVQ9bHuxl45PXTKsH46/5TBKhlR0BS+RvTP63Bs\ncePjRT2rkDraZ8GY+hDu2OwhSRdKGiJpp6RvS8qVJHe/J7bsxU8VzMQ8JOmT7t7hJpWlpaXeci/L\n1atX6/TTT5ckfedPK7Vq2/42v3/p21U6Wt9w3PleOT00dWzrXYcTR/XXt6+c1OZ9btq0SVdccYVW\nrFihxYsX633ve59WrFjRtDTFnj17NGjQINXU1GjatGl65plnNHjw4GaB7NRTT1VZWZmmTJmia665\nRldddZU+9rGPtfp48T8vACA9tWwAkKS83B664YJxGtqvd1OX47od1Tp49Ng1Ywblq2R4f02IC17F\nQ/oqN4e13DORmS1x99KOrgtzluV1Hdzukr4Q1uO3pbUw1t75rpg+fXqzdcJ+8pOf6A9/+IMkacuW\nLXrzzTc1ePDgZt8zbtw4TZkyRZJ0zjnnaNOmTUmrBwCQWodr6/X9VmY3Hq5t0M8WbZAkDeyTq5IR\nBfpQ6ZimVq/ThheoX++MGN6NJMu6f/X2WrKk9gcV/v6z5yelhr59+zZ9vXjxYv3973/Xiy++qD59\n+ujCCy9sdR2x3r17N32dk5OjmppoZ3sAADrW0OB6e8+hYzMbd+7Xmh3V2rTroNqY3ChJeuXrF2to\nQW+WMkKTrAtkHQljMF9BQYGqq6tbvW3fvn0aOHCg+vTpozVr1uill17q8uMAAKKz68CRuMH1wXiv\ndTsPNL2fmEljB/VRyfACXTF5pO5/aXObsxuH9c9LdflIc90ukIUxmG/w4MGaMWOGzjjjDOXn52v4\n8OFNt1166aW65557dPrpp6ukpETnnXfeCf8MAIDw1Byt17qdcTMbdwbha9eBo03XDO7bSyUjCvTh\n6WNiY73667Th/dSn17G31ZOH9kvL2XxIT6EN6g9LR4P6u4Pu9vMCyCzpsk9gR3XUN7g27z7YfDHV\nndXatPugGt8a83J76LThBSoZ3jjAvr9KRhRoaEHvNh61czUg+0U+qB8A0P2EsbRQsur46qPLtHht\nhXrm9NDaHdV6s6Jah2uDCV09TCoe3FcTRhRo1pRRTa1eYwf1UU6Pro/zmj21iACGhBDIAAAn7Ehd\nvTZUHNR3/rSy1X0Cv/bYMj1StqWN706+JZv36khd89nzR+sbtOD1bRrSr7dOH1mgj517UlOr1/jh\n/ZSXm5Oy+oCWCGQAgIS5B4uYNnbvNQ5w31h5sM09EyXpSF2DapO4vFBHWoaxRiap7JvvSVkdQKII\nZACAVlUdOto0tqoxeK3beUAH4vZMHD0wXxNGFOi9E4erZER/fe/Pq1RRfeS4+yoqzNf/3viOlNWe\nrvsVAm0hkAFAN3e4Nm7PxLhWr537jwWrAfnBIqZXnx23Z+LwAhXE7ZkoBetypcPMwnTdrxBoC4EM\nAE5Qusyk66iORPdMPHVYP804ZUjcnon9Nbx/YouYpss+gelSB5Aolr2IQL9+/XTgwIEuf3+m/bxA\nNmttv8L83BzdcfXkSGcVSlKvnj10xeQR6tUzJ9gzcWe1DsXtmTh2UJ+m1q6mPRMH91VP9kwEkoZl\nL9qz7BHpH7dL+7ZKA0ZLF39LOvOaqKsCkIF+8OSaVmcVzn30Df3mhU0pq2PVtn2qrW/+B/bRugbN\nX7qtac/Ea0rHNIWv04YXqC97JgJpo/v9b1z2iPSnm6Xa2GDPfVuCY6nLoezWW2/VmDFj9IUvBHul\n33bbbeqYMMapAAAgAElEQVTZs6cWLVqkvXv3qra2Vt/73vc0a9asZPwEACLk7lqzo1pPr6nQojUV\n2r7v+L1pJam23lWYn9vqbWFoGcYamaTX/r/3smcikOayL5D99VZpx/K2b9/6qlTfYgZQbY30x5uk\nJb9r/XtGTJYuu7PNu7z22mv1pS99qSmQPfLII1q4cKFuvvlm9e/fX7t27dJ5552nq666il+KQAaq\nOVqv59fv0tNrK7R4TYW2xULYpFH9VdC7p6rjZh02KirM1+8+NT1lNbY3q5DfO0D6y75A1pGWYayj\n8wmYOnWqKioqtG3bNlVWVmrgwIEaMWKEvvzlL+vZZ59Vjx49VF5erp07d2rEiBFdfhwAqbNlzyEt\nWluhp9dU6IUNu3W0rkF9euXoglOH6OaLx+vdE4ZpeP+8NseQMasQQGdkXyBrpyVLkvSjM4JuypYG\njJE++ZcuP+yHPvQhPfroo9qxY4euvfZaPfDAA6qsrNSSJUuUm5ur4uJiHT7cetcGgOjV1jdoyea9\nWrQmCGFvVgQTb4oH99FHzx2riyYM0/Rxg9S7Z/PV3NNlNl+61AGga7IvkHXk4m81H0MmSbn5wfkT\ncO211+ozn/mMdu3apWeeeUaPPPKIhg0bptzcXC1atEibN28+wcIBJNvuA0e0eG2lnl5boWfXVar6\ncJ1yc0zTxw3StdPG6KIJw3Ty0H4d3k+67FeYLnUA6LzuF8gaB+4neZblpEmTVF1draKiIo0cOVIf\n/ehHdeWVV2ry5MkqLS3VhAkTklA8gBPh7lq5bX/QCra2Qq9vqZK7NKRfb112xghdNGGYZpw65LjF\nTgEgbN0vkElB+AphmYvly49NJhgyZIhefPHFVq87kTXIAHTOwSN1em79Li1aU6FFayuaVp8/a/QA\n3XLxeF00YZjOGDVAPXow8B1AdLpnIAOQFdpamX7TroPBshRrK/Tyxj06Wt+ggt499c7ThujdJcN0\nYckwDS3oHXX5ANCEQAYgI7Wc3VheVaOv/O8b+v4Tq1RZfVSSdMrQvvrEO07SuycM07TiQcplBXoA\naSprApm7d4u1djJtqysgDIdr6/W9v6w6boX8+gbX/po63XblRF00YbjGDu4TUYUA0DlZEcjy8vK0\ne/duDR48OKtDmbtr9+7dysvLi7oUIKWqDh3Vks179eqmvSrbtEfLtu7T0fqGVq89Wteg62eMS3GF\nAHBisiKQjR49Wlu3blVlZWXUpYQuLy9Po0ePjroMIDTurq17a1S2eU9TAFu3M5gIk5tjmlw0QJ+c\nUaz/XbJVew4ePe77RxXmp7pkADhhWRHIcnNzNW4cfxEDmai+wbVmx36VbdqrVzftUdmmvdqxP1hE\nuaB3T51TPFCzphSp9KSBOmtMofJyg4VZTx/Zn5XpAWSNrAhkADJHzdF6vbG1SmWb9uiVTXu1dPPe\npr0gRw7I0/RxgzSteKBKiwfptOEFymljOQpWpgeQTQhkAEK15+BRlW3ao7LNQQvYivJ9qq13mUkl\nwws0a+ooTSsepNLiQSrqZHcjK9MDyBYEMgCd1tb6X+6ut/ccahr79eqmPdpQeVCS1Cunh84aM0Cf\nfufJmlY8UOeMHaQBfVgRHwAkyTJtGYXS0lIvKyuLugyg22q5/pcUDLafOLJA2/YdUWV1sBL+gPxc\nlZ4UdD1OKx6oM4oGNI3/AoDuwsyWuHtpR9fRQgagU+5auPa49b9q610rtlXrqrNGqbR4oKYVD9Kp\nQ/uxHREAJIhABiBh6yuqVV5V0+ptDQ2uH107JcUVAUB2CHUfETO71MzWmtl6M7u1ldvHmtkiM1tq\nZsvM7PIw6wHQNUvf3qs595XpPT98ts1rWP8LALoutBYyM8uR9DNJ75W0VdKrZva4u6+Ku+ybkh5x\n97vNbKKkJyQVh1UTgMS5u559c5fuXrxeL23cowH5ubr54vEaVtBb3//Latb/AoAkCrPLcrqk9e6+\nUZLM7GFJsyTFBzKX1D/29QBJ20KsB0AC6htcf12xXXcv3qCV2/ZrRP88ffN9p+u66WPVt3fwK6Nf\n756s/wUASRRmICuStCXueKukc1tcc5ukv5nZFyX1lfSe1u7IzOZImiNJY8eOTXqhAIINu+e/Vq5f\nPLtBm3cf0slD++rfP3CmZk8tUq+ezUc3sP4XACRX1IP6r5P0W3f/DzM7X9L9ZnaGuzfbNdjd75V0\nrxQsexFBnUDWqj5cqwdeflu/eu4tVVYf0VmjB2jex87WeyeOaHOVfABAcoUZyMoljYk7Hh07F+8G\nSZdKkru/aGZ5koZIqgixLgCSKquP6DfPv6X7X9qs6sN1euf4IfrxtVN0/imDZUYQA4BUCjOQvSpp\nvJmNUxDEPizpIy2ueVvSxZJ+a2anS8qTVBliTUC3t2XPIf3i2Q16pGyrausbdPkZI3Xju07R5NED\noi4NALqt0AKZu9eZ2U2SFkrKkfRrd19pZrdLKnP3xyV9RdJ/m9mXFQzwv94zbesAIEOs3r5f9zyz\nQX9etl05ZvrAOUX6zDtP1slD+0VdGgB0e6GOIXP3JxQsZRF/7ltxX6+SNCPMGoDu7pW39ujuxeu1\naG2l+vbK0Q0XjNMNF4zT8P55UZcGAIiJelA/gBA0NLgWra3Q3Ys3qGzzXg3u20v/dslp+vh5xWzo\nDQBpiEAGZJHa+gb9edk23bN4o9burFZRYb5unzVJHzpnjPJ7sbE3AKQrAhmQBWqO1uuRsi2699mN\nKq+qUcnwAv3ntVP0vjNHKjcn1B3SAABJQCADMsyCpeVNq+SPGJCnqWMK9dJbe7Tn4FGVnjRQ3509\nSe8uGcbSFQCQQQhkQAZZsLRc8+Yvb9pHcvu+w9q+b4cmjizQLz5+jqYVD4q4QgBAV9CXAWSQuxau\nbbapd6N9NXWEMQDIYAQyIIOUV9W0en5bG+cBAJmBQAZkiGfXtb2JxajC/BRWAgBINgIZkAGeXVep\nz9xXplED8pSX2/y/bX5ujubOLImoMgBAMhDIgDT37LpKffq+Mp08tJ/+cvM7defVZ6qoMF8mqagw\nX3dcPVmzpxZFXSYA4AQwyxJIY8/EWsZOHdpPD3z6XA3s20uzpxYRwAAgyxDIgDS1eG2F5ty/ROOH\n9dP/3BCEMQBAdiKQAWkoPow98OlzVdiHMAYA2YwxZECaIYwBQPdDIAPSyKK1FZpz3xKdNpwwBgDd\nCV2WQJpYtKZCn71/iU4bEYwZI4wBQPdBCxmQBghjANC90UIGROzpNTt14/2vqWREgf7nhnM1oE9u\n1CUBAFKMFjIgQv9YHYSxCSMJYwDQnRHIgIj8Y/VO3fg/SzRhZIHu/xRhDAC6M7osgQg0hrHTR/bX\n/TecqwH5hDEA6M5oIQNS7O+rgjA2kTAGAIghkAEp9NSqnfrcA0EYu48wBgCIocsSSJG/rdyhLzz4\nmiaOGqD7PjWdMAYAaJJQC5mZzTez95kZLWpAF8SHsftvIIwBAJpLNGD9XNJHJL1pZneaWUmINQFZ\npTGMTYqFsf55hDEAQHMJBTJ3/7u7f1TS2ZI2Sfq7mb1gZp80M95dgDYsXLlDn38gCGP3EcYAAG1I\nuAvSzAZLul7SpyUtlfRjBQHtqVAqAzLckyt26AsPvKbJowljAID2JTSo38z+IKlE0v2SrnT37bGb\nfm9mZWEVB2SqJ1fs0E0PxsLYp6argDAGAGhHorMsf+Lui1q7wd1Lk1gPkPGeXLFdNz24lDAGAEhY\nol2WE82ssPHAzAaa2ec7+iYzu9TM1prZejO7tY1rrjGzVWa20sweTLAeIC39dXkQxs4kjAEAOiHR\nQPYZd69qPHD3vZI+0943mFmOpJ9JukzSREnXmdnEFteMlzRP0gx3nyTpS52oHUgrf12+XTc9tFRn\njSnU7whjAIBOSDSQ5ZiZNR7EwlavDr5nuqT17r7R3Y9KeljSrBbXfEbSz2IBT+5ekWA9QFppDGNT\nCGMAgC5INJA9qWAA/8VmdrGkh2Ln2lMkaUvc8dbYuXinSTrNzJ43s5fM7NLW7sjM5phZmZmVVVZW\nJlgykBpPtAhj/XqzAQYAoHMSfef4mqTPSvpc7PgpSb9M0uOPl3ShpNGSnjWzyfHdo5Lk7vdKuleS\nSktLPQmPCyTFX5Zt180PL9XUMYX6LWEMANBFCb17uHuDpLtjH4kqlzQm7nh07Fy8rZJedvdaSW+Z\n2ToFAe3VTjwOEIk/L9umWx5+XWePLdRvPkkYAwB0XaLrkI2XdIeCwfl5jefd/eR2vu1VSePNbJyC\nIPZhBdsvxVsg6TpJvzGzIQq6MDcmXD26hQVLy3XXwrXaVlWjUYX5mjuzRLOntuz9Tm0dhX1yVXWo\nVqXFAwljAIATlui7yG8kfVvSjyS9W9In1cH4M3evM7ObJC2UlCPp1+6+0sxul1Tm7o/HbrvEzFZJ\nqpc01913d+1HQTZasLRc8+YvV01tvSSpvKpG8+Yvl6SUhrKWdew9VKseJn3g7NGEMQDACTP3jodk\nmdkSdz/HzJa7++T4c6FX2EJpaamXlbE5QHcx486nVV5Vc9z5gX1yddtVk1JWx22Pr9TeQ7XHnS8q\nzNfzt16UsjoAAJkllpc6XEQ/0T/tj5hZD0lvxlq9yiX1O5ECgURsayWMSUEL1S0Pv57iao7XVn0A\nAHRGooHsFkl9JN0s6bsKui0/EVZRQKNRhfmttpANK+ith+acl7I6rrv3JVVUHznu/KjC/JTVAADI\nXh0GstgisNe6+79JOqBg/BiQErOmjNLPF29odi4/N0dfv/x0nTI0dY20X7/89GZjyBrrmDuzJGU1\nAACyV4eBzN3rzeyCVBQDxDt4pE5/WrZNQ/v1Um5OD23fdziyWZaNj5cOsz0BANkn0S7LpWb2uKT/\nlXSw8aS7zw+lKkBB+Nm6t0aPfPZ8TSseFHU5mj21iAAGAAhFooEsT9JuSfHTyVwSgQyheHXTHv3u\nxU36xPnFaRHGAAAIU6Ir9TNuDClzuLZeX3t0mYpi3YIAAGS7RFfq/42CFrFm3P1TSa8I3d6P/r5O\nG3cd1AOfPld9WXQVANANJPpu9+e4r/MkvV/StuSXg+7ujS1V+u9nN+q66WM049QhUZcDAEBKJNpl\n+Vj8sZk9JOm5UCpCt3Wkrl5zH31DwwryNO/y06MuBwCAlOlqf9B4ScOSWQjws0UbtG7nAf36+lL1\nz8uNuhwAAFIm0TFk1Wo+hmyHpK+FUhG6pVXb9uvni9br/VOLdNGE4VGXAwBASiXaZVkQdiHovurq\nG/TVx95QYZ9cfeuKiVGXAwBAyvVI5CIze7+ZDYg7LjSz2eGVhe7k3n9u1Iry/frurDM0sG+vqMsB\nACDlEgpkkr7t7vsaD9y9StK3wykJ3cn6imr959/f1OWTR+iyySOjLgcAgEgkGshau44FonBC6htc\nX310mfr0ytF3rjoj6nIAAIhMooGszMx+aGanxD5+KGlJmIUh+/32hU167e0q3XblJA0t6B11OQAA\nRCbRQPZFSUcl/V7Sw5IOS/pCWEUh+23efVB3LVyjiycM06wpo6IuBwCASCU6y/KgpFtDrgXdREOD\n62uPLVNujx76/vsny8yiLgkAgEglOsvyKTMrjDseaGYLwysL2ezBV97WSxv36BvvO10jBuRFXQ4A\nAJFLtMtySGxmpSTJ3feKlfrRBeVVNbrjidWacepgXTttTNTlAACQFhINZA1mNrbxwMyK1XzlfqBD\n7q5585fLJd159Zl0VQIAEJPo0hXfkPScmT0jySS9U9Kc0KpCVnrstXI9u65S37lqksYM6hN1OQAA\npI1EB/U/aWalCkLYUkkLJNWEWRiyS8X+w7r9Tys1rXigPn7eSVGXAwBAWkl0c/FPS7pF0mhJr0s6\nT9KLki4KrzRkC3fXNxas0JG6Bv3gA2eqRw+6KgEAiJfoGLJbJE2TtNnd3y1pqqSq9r8FCPx52XY9\ntWqnvnLJaTp5aL+oywEAIO0kGsgOu/thSTKz3u6+RlJJeGUhW+w+cETffnylzhpTqBsuODnqcgAA\nSEuJDurfGluHbIGkp8xsr6TN4ZWFbHHbn1ap+nCt7vrgmcqhqxIAgFYlOqj//bEvbzOzRZIGSHoy\ntKqQFRau3KE/vbFN/+e9p+m04QVRlwMAQNpKtIWsibs/E0YhyC77DtXqmwtW6PSR/fW5C0+JuhwA\nANJapwMZkIjv/mWV9hw8qt9cP025OYkOVQQAoHsK9Z3SzC41s7Vmtt7M2tyc3Mw+YGYeW+sMGe6Z\ndZV6dMlW3fiuk3VG0YCoywEAIO2FFsjMLEfSzyRdJmmipOvMbGIr1xUoWFbj5bBqQepUH67VvMeW\n6dRh/fTFi8ZHXQ4AABkhzBay6ZLWu/tGdz8q6WFJs1q57ruSfiDpcIi1IEV+8OQabd9/WP/+wTOV\nl5sTdTkAAGSEMANZkaQtccdbY+eamNnZksa4+1/auyMzm2NmZWZWVllZmfxKkRQvbtit/3npbX1q\nxjidPXZg1OUAAJAxIhttbWY9JP1Q0lc6utbd73X3UncvHTp0aPjFodMOHa3T1x5bppMG99G/XcKa\nwQAAdEaYgaxc0pi449Gxc40KJJ0habGZbVKwP+bjDOzPTP/xt3V6e88h3Xn1mcrvRVclAACdEWYg\ne1XSeDMbZ2a9JH1Y0uONN7r7Pncf4u7F7l4s6SVJV7l7WYg1IQRLNu/Vr59/Sx87b6zOP2Vw1OUA\nAJBxQgtk7l4n6SZJCyWtlvSIu680s9vN7KqwHhepdbi2Xl999A2NGpCvWy87PepyAADISKEuDOvu\nT0h6osW5b7Vx7YVh1oJw/OQfb2pD5UH97lPT1a836wwDANAVLKGOLltRvk+/eHajPnTOaL3rNCZb\nAADQVQQydMnRugb92/++ocF9e+mb7ztuvV8AANAJ9DGhS+55ZoPW7KjWvR8/RwP65EZdDgAAGY0W\nMnTa2h3V+q+n39SVZ43SJZNGRF0OAAAZj0CGTqmrb9BXH31DBXm5uu1KuioBAEgGuizRKb967i29\nsXWf/uu6qRrcr3fU5QAAkBVoIUPCNlQe0H88tU6XTByuK84cGXU5AABkDQIZEtLQ4Prao8uU17OH\nvjf7DJlZ1CUBAJA1CGRIyH0vblLZ5r361pWTNKx/XtTlAACQVRhDhjYtWFquuxau1baqGknShBEF\n+sDZRRFXBQBA9qGFDK1asLRc8+YvV3lVjVySS3pr10H98fVtUZcGAEDWIZChVXctXKua2vpm547U\nNeiuhWsjqggAgOxFIMNxNu06qPJYN2VL29o4DwAAuo4xZGiyonyf7nlmg55Yvr3Na0YV5qewIgAA\nugcCWTfn7nr5rT26e/EGPbOuUv1699Rn/uVkFQ3I0x1/bd5tmZ+bo7kzSyKsFgCA7EQg66YaGlx/\nX71Tdz+zQUvfrtKQfr00d2aJPnbeSRqQH2wW3j+/V9Msy1GF+Zo7s0SzpzLLEgCAZCOQdTO19Q36\n4+vbdM8zG7S+4oDGDMrXd2efoQ+dM1p5uTnNrp09tYgABgBAChDIuolDR+v08Ctb9Mt/btS2fYc1\nYUSBfvzhKXrf5JHqmcPcDgAAokQgy3J7Dx7VfS9u1m9feEt7D9VqevEgff/9k3VhyVC2PwIAIE0Q\nyLLU9n01+uU/39JDr7ytQ0fr9Z7Th+nGd52i0uJBUZcGAABaIJBlmfUVB/SLZzZowevlanDpqrNG\n6cZ3naKSEQVRl4ZssuwR6R+3S/u2SgNGSxd/SzrzmqirAoCMRSDLEq9vqdLdi9frb6t2qldOD31k\n+lh9+p0na8ygPlGXhmyz7BHpTzdLtbFFgvdtCY4lQhkAdBGBLIO5u55bv0t3L96gFzbsVv+8nrrp\n3afqE+8o1pB+vaMuD9nqH985FsYa1dZIj39R2vScNGBM0GrW+NG/SOrZK5paASBDEMgyUH2D68kV\nO3T3M+u1ony/hvfvrW9cfrquO3es+vXmnxQh2bVeeu23QTdla+oOS2ufkA5WtrjBpH7Dm4e0ZqFt\njNRnkMQkEwDdGO/eGeRIXb3mv1auXzyzQZt2H9K4IX31gw9M1uypRerdM6fjOwA6q+6ItPpP0pLf\nSpv+KfXoKfXMl+pa2dN0wBjpyyuC1rL924KuzH1bYx+xr3eukNY9GYS3eLl92glsja1srbT6MpYN\nQJYgkKWhBUvLm62Q/8WLTtH+w3X65T/fUkX1EU0uGqC7P3q2Lpk0Qjk9aFVACCrXSa/9Tnr9Qalm\njzSwWLr429KUj0pvPdN8DJkk5eYHYajx68GnBB+tcZcO7W4R2OJD20rpwM7jv69ZK9uYoCVu5R+k\n+qPB7YxlA5DBzN2jrqFTSktLvaysLOoyQrNgabnmzV/ebA/JRjNOHazPvetUzTh1MGuIIflqD0ur\nHw9awzY/H7SGTXifdM710rgLpR5xCwiH3TJVd0TaXx7cf9WW5oGt8aO1VjpJ6jNY+vxLUr9hyasH\nALrIzJa4e2mH1xHI0seho3X6l39fpF0Hjh5329B+vfXqN98TQVXIepVrpSW/k954UKrZG7SGnXN9\n0BqWrqHGXfrOQEnt/P4aPF4qniGddEHwuf+olJUHAI0SDWR0WUZo14EjKtu0V2Wb9ujVzXu1snyf\n6hpaf4PZdeBIiqtDVqutkVbFWsPefkHqkSudfkUQxIr/pXlrWDoyC1rm9m05/rZ+w6TzvhC08q2Y\nH/yMkjRwnFR8QfBx0gypcExKS0YEGGOIDEIgSxF316bdh/Tqpj0q27RHZZv2auOug5KkXj17aMqY\nQn32XSfr4Ve2aPfB41vIRhXmp7pkZKOKNUFAeeMh6XCVNOhk6T3fibWGDY26us65+Futj2W75PvB\nm+4FX5Ia6qUdy6RNzwcBbfWfpKX3B9cWjj3WenbSjKBlkKEA2YP18ppLl3BKHW0ikIWktr5Bq7bt\njwWwvSrbvKepK7KwT65KTxqka6eNUWnxIJ1R1L9pluT4YQXHjSHLz83R3JklkfwcyAK1NdKqP0pl\nv5G2vBRrDbsy1hr2zvRvDWtL4y/P9n6p9siRRk0NPt5xk9TQIFWsjAW056Q3FwZdtVIwk/OkGce6\nOQefQkDLFO7S0YPSoV3Swd3B579+rfX18p68VcofJPXuJ/UukHrFPvcukHJyk19bOrzxp0s4pY52\nhTqGzMwulfRjSTmSfunud7a4/f9I+rSkOkmVkj7l7pvbu890HUN24Eidlr69V6/GuiCXvl3VFKrG\nDMrXtOJBsY+BOnlIP/VoZ3Zky1mWc2eWaPbUolT9KMgWO1cFMyXfeEg6vE8adEpsbNhHpL5Doq4u\nPTQ0SLvWBgvabn4+CGoHK4Lb+o2QTnrHsYA2tISAlqgTDSHuwWv20G7p4K5Y0NrVPHC1PG65lEpX\n9MyLC2j9pN79WxwXSL0K2jhucS6n5/Fv/FLQinvlTxJ/Ptyl+lqpoU5qqA1afTt7/Icbg+eopfyB\nwb9NQ73kDbHP9XGfG1ocd+V83P2+9Wzr/045vaUx07v2b9YVW16R6lsZBtS4dE+SRT6o38xyJK2T\n9F5JWyW9Kuk6d18Vd827Jb3s7ofM7HOSLnT3a9u733QJZBX7D6ts896mFrBV2/ervsHVw6TTR/bX\ntOJBKi0eqNKTBmnEgLyoy0UypMNfuh05ekhatSDoltzyspTTK9Ya9slg7BSBon3u0u71QUBrDGnV\n24Pb+gyJBbTYGLRhE4+1LmbCayNV2gohM/9vEGwTCVcHdwVhojW5fYJ/i76DY5+HBDNr+w6JOx4i\nPfLxY/928fqNkK69XzqyXzpyQDpSLR2NfW78aDo+EFwXf9zW7N6WeuYHb/recPxtPXKlwafGAlOd\nVF8XF6BaHLf2/VGwnKDFudnnHq2c79H2ddtfb/v+T5qRup9l8/Nt3GDSbVVJf7h0CGTnS7rN3WfG\njudJkrvf0cb1UyX91N3b/VcJO5C11jo1a8oobag8GAy+j3U/bt59SJKUl9tDU8cM1LTigSotHqSp\nYwtVkBdCs3d3lg5vdsn4SzeZtbR8PoZPio0N+710ZF/wy/6c66WzrqM17ES4S3s2Hms92/z8sYkE\n+QOlse+QevWVVv8xWKqjUVSvjSgdPSRVrpb+5wPBbN1E9e7fIlANbh6sWgauXgnuzxvW/9n6Oulo\ndVxgq27lOBbkXvivtu/n9KuCpWVycoPPjR+tHucEIa6rxw9/pPW1/QpGSp9Z1EHAyknesIYfndH6\nRJyQWqbSpY50CGQflHSpu386dvxxSee6+01tXP9TSTvc/Xut3DZH0hxJGjt27DmbN7fbq9llra0B\n1sOkvJ49dKg2+CtlcN9eKi0eGGsBG6RJo/orNydDx+BkgtZ+qeb0lmbcHIx/aqg79tHUTN/ecX3c\nX6GdON70z+ZvuI1y+0hnfCD4Rd8z78Q+90hgt4XWng/rEfwVndNLmjgrCGInzaA1LCx7N8cFtOek\nvZtav67PEOnTT0kDxgbdV9mioV7a81YwFm9n7KNiVXCuvWVIJOnqXx7fstXaDgzJEvUfc+kSQNLl\nD8puWkdGBTIz+5ikmyS9y93bXd8hzBayGXc+rfKq45uj+/TK0W1XTlJp8UCNG9KXRVlT6YeTpP1t\n7J14Ipr+Ao39Ndn0V2nL49jHttfavq+CkcF/7LrDJzaOpUduXEDLC7o8Wn5+a/HxA5UlKa9Q+uJr\nwZsdUuu2QrUbRHrkBjM4B58StFwOOjn4PPjU4LWTrpMq3KUDFbHgtSoIXTtXBuvWNXbbWY/g5xk2\nMWilHTZR+utXW+8qTHUISQfpEkAaa4m6p6Gb1pEO65CVS4pf6Gd07FwzZvYeSd9QAmEsbNtaCWOS\nVHO0XtdMY82ilGpokJY/0k4YM+n6v8Q16ycasGLHnQ3Vif6l6x6EssaA1vT5cPAm1tXPRw8FA5xb\nC2NSMACaMBaNttZD6zss+CW/Z0MwLm33Rmnj4uahvWdsm6mmkNYY2k4JWpBS9cffkQNS5ZpjrV2N\nn7GL+lQAAAskSURBVA/tbv7zDJ8kTbshFsAmSkMnBAEjXt3h9rfW6k4SmQmcylrSoQudOtoUZiB7\nVdJ4MxunIIh9WNJH4i+IjRv7hYKWtIoQa0nIqML8VlvIWAMsxTY8LT31LWnH8iBAtTa4d8DoYPZb\nqrS15lXLNxmz4HzLN6lkaTMYjg7n8dCxtl4bM79//C/8hgapelssoG0IPvZsCMLP2ieCrvJGvQcc\n2xO0MaQ1HucNaL2Wjv7qr68LHq8peK0KWsDiu11z+0rDJkgllx9r9Ro+KfGxiOkUQtJBGr7xIz2F\nvezF5fp/7d17bN1lHcfx94e2jN2yMjZwWyfjFnTcNkACTvAyRFRgBPAKBC9/GIPIlAQZKhCCQhTd\nQImMADLDgpfJIlmiMgaZEOXmbjCGcpNtrHNTYAzYWLd9/eP5lXNO19LK2vOc9nxeyck559fT02+f\ntL9++vyeC8wiLXtxe0T8QNLVwOMRcY+k+4AjgPb+7dURccY7vWdfXrLsbAzZ4KYGrj3rCC87UQ2t\nK1IQe/4BaN6/NB17wcXu8i+voVYugVhJb/xs7NgOr76YJhG8HdiK+01rqLgsOnR0EdAOhn2K3rX/\nPgeLf1Q5C7BhUJpl29BUutzYPt1fe6TPK7/cuN9EaJ5Qu5dRzfqh7GPI+kqOWZYOY33s1dVw/zXp\nj9rgZjjp0nRZpH2wby0EoVri9qg/bVvhlRdKIe3l50o9bK+v7/7zh48pBa59D0v3ow5NYxPNrE85\nkFnt2/IKPPgTeGR2+m/9+K/DlOkplJlZz7y1OfWqzT6pixf0zdpKZtYztTCo36xzbVvh0Vvgweth\n62tpH8WPXg4j3BNp9n8bNBzGHJUmmHh8oVm/5UBm1dM+c/L+a9IfjkNOgZOvSuNXzGz39HTiiZnV\nJAcyq47ymZNjJsG0m+DAD+euymzg8OxGs37Ngcz6VutyWHhlaebk2bfBYWd5FpdZX/ASC2b9lgNZ\nR57B1js6zpz8xLWVMyfNzMzsbQ5k5Tqu8bRpTXoODmU91XHm5Ieme+akmZlZNxzIyi26etetadq2\npOMOZO+s05mTMzzDy8zMrAccyMpt6mLfxE1r4M6z02D0sZPS/YiW6u0zV8s8c9LMzGy3OZCV62qT\n4KYh8ForPPcARLGt0pBRae2fsZPrN6R55qSZmVmvcCAr19U6PqffkC5Zbnsz7QfXugzWLUv3D80s\nC2n7VPaijZ2UFmscaCHNMyfNzMx6lQNZue7W8dlzCIz/QLq1a9sC65/sENJmlULa4JGVAW3MJGh+\nb/8IaR1nnJ5wIaxb6pmTZmZmvcx7WfaFti2pJ23d0iKoLYeNq2Dn9vTxwSOLy53tQW1yZUirhaU3\nOs44badGmHKRZ06amZn1gPeyzKlpMLQcm27t2rYWlzuXlnrS/vqzspC2dwpnjYPS2Kwd29Lx9qU3\nYidMnJaO72gr7nvrcSfHVi2A7Vt2/d6GjU6D9s3MzKzXOJBVS9Ne0HJMurVr2wobVpYC2rplsH7F\nrp/btgXmfy3delvDnsWtqfJxZ2EMYPP63q/BzMyszjmQ5dS0F4w7Jt3aXdUMdHEZeeqVXQeo9seN\n3Xy8/PEejV2PZZt5eOczTr2umJmZWa9zIKs1XS29MWI8nPjt6tXR1YzTqVdUrwYzM7M64XUKas3U\nK1LwKZcjCB35WTj9xhQEUbo//UbvWGBmZtYH3ENWa7pbeqPatTiAmZmZ9TkHslrkIGRmZlZXfMnS\nzMzMLDMHMjMzM7PMHMjMzMzMMnMgMzMzM8us3+1lKWkj8GIVvtQo4D9V+Dr9gduiktujxG1Rye1R\nye1R4raoVE/tsX9EjO7uRf0ukFWLpMd7shloPXBbVHJ7lLgtKrk9Krk9StwWldweu/IlSzMzM7PM\nHMjMzMzMMnMg69otuQuoIW6LSm6PErdFJbdHJbdHiduiktujA48hMzMzM8vMPWRmZmZmmTmQmZmZ\nmWXmQNaBpFMl/UPSs5Iuy11PTpLGS3pA0lOSVkq6OHdNuUlqkLRU0oLcteQmqVnSPElPS1ol6YTc\nNeUk6VvF78mTku6StFfumqpJ0u2SNkh6suzYSEkLJT1T3O+ds8Zq6aItflz8rqyQNF9Sc84aq6mz\n9ij72CWSQtKoHLXVEgeyMpIagJuATwITgS9Impi3qqy2A5dExETgeODCOm8PgIuBVbmLqBE3AH+K\niPcBR1HH7SJpHPBN4NiIOBxoAD6ft6qquwM4tcOxy4BFEXEIsKh4Xg/uYNe2WAgcHhFHAv8EZlS7\nqIzuYNf2QNJ44BRgdbULqkUOZJWOA56NiOcjYhvwa2Ba5pqyiYjWiFhSPN5M+oM7Lm9V+UhqAT4N\n3Jq7ltwkjQBOAm4DiIhtEfFq3qqyawQGS2oEhgDrMtdTVRHxF+DlDoenAXOKx3OAM6taVCadtUVE\n3BsR24unDwMtVS8sky5+NgBmApcCnl2IA1lH44A1Zc/XUscBpJykCcBk4JG8lWQ1i3Ty2Jm7kBpw\nALAR+GVxCfdWSUNzF5VLRLwEXE/6T78V2BQR9+atqibsFxGtxeP1wH45i6khXwH+mLuInCRNA16K\niOW5a6kVDmTWLUnDgN8D0yPitdz15CDpNGBDRPw9dy01ohE4GvhFREwG3qB+LkftohgbNY0UVMcC\nQyWdl7eq2hJpjaW67wmR9F3ScJC5uWvJRdIQ4HLgity11BIHskovAePLnrcUx+qWpCZSGJsbEXfn\nriejKcAZkv5FupT9MUl35i0pq7XA2oho7zGdRwpo9epk4IWI2BgRbcDdwAcz11QL/i1pDEBxvyFz\nPVlJ+hJwGnBu1PcioAeR/nlZXpxTW4Alkt6TtarMHMgqPQYcIukASXuSBuXek7mmbCSJNEZoVUT8\nNHc9OUXEjIhoiYgJpJ+L+yOibntAImI9sEbSocWhqcBTGUvKbTVwvKQhxe/NVOp4kkOZe4ALiscX\nAH/IWEtWkk4lDXk4IyLezF1PThHxRETsGxETinPqWuDo4rxStxzIyhQDLr8B/Jl0Mv1tRKzMW1VW\nU4DzSb1By4rbp3IXZTXjImCupBXAJOCHmevJpugpnAcsAZ4gnVvramsYSXcBfwMOlbRW0leB64CP\nS3qG1It4Xc4aq6WLtvg5MBxYWJxLb85aZBV10R7WgbdOMjMzM8vMPWRmZmZmmTmQmZmZmWXmQGZm\nZmaWmQOZmZmZWWYOZGZmZmaZOZCZmfWQpI9IWpC7DjMbeBzIzMzMzDJzIDOzAUfSeZIeLRbgnC2p\nQdLrkmZKWilpkaTRxWsnSXpY0gpJ84t9KZF0sKT7JC2XtETSQcXbD5M0T9LTkuYWK/Obme0WBzIz\nG1AkvR/4HDAlIiYBO4BzgaHA4xFxGLAYuLL4lF8B34mII0mr7LcfnwvcFBFHkfalbC2OTwamAxOB\nA0k7WpiZ7ZbG3AWYmfWyqcAxwGNF59Vg0qbWO4HfFK+5E7hb0gigOSIWF8fnAL+TNBwYFxHzASJi\nK0Dxfo9GxNri+TJgAvBQ339bZjaQOZCZ2UAjYE5EzKg4KH2/w+ve7b5xb5U93oHPo2bWC3zJ0swG\nmkXAOZL2BZA0UtL+pPPdOcVrvgg8FBGbgFcknVgcPx9YHBGbgbWSzizeY5CkIVX9Lsysrvg/OzMb\nUCLiKUnfA+6VtAfQBlwIvAEcV3xsA2mcGcAFwM1F4Hoe+HJx/HxgtqSri/f4TBW/DTOrM4p4t732\nZmb9h6TXI2JY7jrMzDrjS5ZmZmZmmbmHzMzMzCwz95CZmZmZZeZAZmZmZpaZA5mZmZlZZg5kZmZm\nZpk5kJmZmZll9j+V/OM+DPVnygAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fd62e554e50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('loss')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(solver.train_acc_history, '-o')\n",
    "plt.plot(solver.val_acc_history, '-o')\n",
    "plt.legend(['train', 'val'], loc='upper left')\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('accuracy')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train the net\n",
    "By training the three-layer convolutional network for one epoch, you should achieve greater than 40% accuracy on the training set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(Iteration 1 / 980) loss: 2.304740\n",
      "(Epoch 0 / 1) train acc: 0.103000; val_acc: 0.107000\n",
      "(Iteration 21 / 980) loss: 2.132736\n",
      "(Iteration 41 / 980) loss: 1.890223\n",
      "(Iteration 61 / 980) loss: 1.790478\n",
      "(Iteration 81 / 980) loss: 1.745026\n",
      "(Iteration 101 / 980) loss: 1.892161\n",
      "(Iteration 121 / 980) loss: 1.890149"
     ]
    }
   ],
   "source": [
    "model = ThreeLayerConvNet(weight_scale=0.001, hidden_dim=500, reg=0.001)\n",
    "\n",
    "solver = Solver(model, data,\n",
    "                num_epochs=1, batch_size=50,\n",
    "                update_rule='adam',\n",
    "                optim_config={\n",
    "                  'learning_rate': 1e-3,\n",
    "                },\n",
    "                verbose=True, print_every=20)\n",
    "solver.train()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Visualize Filters\n",
    "You can visualize the first-layer convolutional filters from the trained network by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "grid = visualize_grid(model.params['W1'].transpose(0, 2, 3, 1))\n",
    "plt.imshow(grid.astype('uint8'))\n",
    "plt.axis('off')\n",
    "plt.gcf().set_size_inches(5, 5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Spatial Batch Normalization\n",
    "We already saw that batch normalization is a very useful technique for training deep fully-connected networks. Batch normalization can also be used for convolutional networks, but we need to tweak it a bit; the modification will be called \"spatial batch normalization.\"\n",
    "\n",
    "Normally batch-normalization accepts inputs of shape `(N, D)` and produces outputs of shape `(N, D)`, where we normalize across the minibatch dimension `N`. For data coming from convolutional layers, batch normalization needs to accept inputs of shape `(N, C, H, W)` and produce outputs of shape `(N, C, H, W)` where the `N` dimension gives the minibatch size and the `(H, W)` dimensions give the spatial size of the feature map.\n",
    "\n",
    "If the feature map was produced using convolutions, then we expect the statistics of each feature channel to be relatively consistent both between different imagesand different locations within the same image. Therefore spatial batch normalization computes a mean and variance for each of the `C` feature channels by computing statistics over both the minibatch dimension `N` and the spatial dimensions `H` and `W`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: forward\n",
    "\n",
    "In the file `cs231n/layers.py`, implement the forward pass for spatial batch normalization in the function `spatial_batchnorm_forward`. Check your implementation by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "# Check the training-time forward pass by checking means and variances\n",
    "# of features both before and after spatial batch normalization\n",
    "\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 4 * np.random.randn(N, C, H, W) + 10\n",
    "\n",
    "print('Before spatial batch normalization:')\n",
    "print('  Shape: ', x.shape)\n",
    "print('  Means: ', x.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', x.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to zero and stds close to one\n",
    "gamma, beta = np.ones(C), np.zeros(C)\n",
    "bn_param = {'mode': 'train'}\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization:')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))\n",
    "\n",
    "# Means should be close to beta and stds close to gamma\n",
    "gamma, beta = np.asarray([3, 4, 5]), np.asarray([6, 7, 8])\n",
    "out, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "print('After spatial batch normalization (nontrivial gamma, beta):')\n",
    "print('  Shape: ', out.shape)\n",
    "print('  Means: ', out.mean(axis=(0, 2, 3)))\n",
    "print('  Stds: ', out.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "# Check the test-time forward pass by running the training-time\n",
    "# forward pass many times to warm up the running averages, and then\n",
    "# checking the means and variances of activations after a test-time\n",
    "# forward pass.\n",
    "N, C, H, W = 10, 4, 11, 12\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "gamma = np.ones(C)\n",
    "beta = np.zeros(C)\n",
    "for t in range(50):\n",
    "  x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "  spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "bn_param['mode'] = 'test'\n",
    "x = 2.3 * np.random.randn(N, C, H, W) + 13\n",
    "a_norm, _ = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "\n",
    "# Means should be close to zero and stds close to one, but will be\n",
    "# noisier than training-time forward passes.\n",
    "print('After spatial batch normalization (test-time):')\n",
    "print('  means: ', a_norm.mean(axis=(0, 2, 3)))\n",
    "print('  stds: ', a_norm.std(axis=(0, 2, 3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Spatial batch normalization: backward\n",
    "In the file `cs231n/layers.py`, implement the backward pass for spatial batch normalization in the function `spatial_batchnorm_backward`. Run the following to check your implementation using a numeric gradient check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(231)\n",
    "N, C, H, W = 2, 3, 4, 5\n",
    "x = 5 * np.random.randn(N, C, H, W) + 12\n",
    "gamma = np.random.randn(C)\n",
    "beta = np.random.randn(C)\n",
    "dout = np.random.randn(N, C, H, W)\n",
    "\n",
    "bn_param = {'mode': 'train'}\n",
    "fx = lambda x: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fg = lambda a: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "fb = lambda b: spatial_batchnorm_forward(x, gamma, beta, bn_param)[0]\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(fx, x, dout)\n",
    "da_num = eval_numerical_gradient_array(fg, gamma, dout)\n",
    "db_num = eval_numerical_gradient_array(fb, beta, dout)\n",
    "\n",
    "_, cache = spatial_batchnorm_forward(x, gamma, beta, bn_param)\n",
    "dx, dgamma, dbeta = spatial_batchnorm_backward(dout, cache)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dgamma error: ', rel_error(da_num, dgamma))\n",
    "print('dbeta error: ', rel_error(db_num, dbeta))"
   ]
  },
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   "source": [
    "# Extra Credit Description\n",
    "If you implement any additional features for extra credit, clearly describe them here with pointers to any code in this or other files if applicable."
   ]
  }
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